Digitally-native eCommerce businesses are used to working with their customer data in order to write copy for marketing campaigns, run PPC ads, calculate customer lifetime value, and make decisions based on core metrics within CRM dashboards.
Investment in AI by banks and financial institutions for risk-related functions such as fraud and cybersecurity, compliance, and financing and loans has grown dramatically in the last half-decade compared to customer-facing functions.
Banks are in one of the best positions for leveraging AI in the coming years because the largest banks have massive volumes of historical data on customers and transactions that can be fed into machine learning algorithms. We recently completed our Emerj AI in Banking Vendor Scorecard and Capability Map in which we explored which AI capabilities banks were taking advantage of the most and which they might be able to leverage in the future.
Life sciences companies are likely to begin experimenting further with AI in their workflows in the coming years, but they face challenges in AI adoption due to strict regulations. Machine learning has a "black box" problem, meaning that it's in many cases impossible to know how a machine learning algorithm comes to its conclusions.
AI and machine learning have had successful applications in the financial sector even before the entry of the mobile banking ecosystem. AI is being used to leverage insights from data for financial investing and trading, wealth management, asset management, and risk management.
Have you ever read a blog post or a whitepaper and heard the terms "data science" or "predictive analytics" used in ways that aren't quite right? As it turns out, terms like these are often used incorrectly, but by the end of this episode of the AI in Industry podcast, you'll have greater clarity about five key terms in AI and data science that are sometimes overused in conversations about AI in the enterprise.
Artificial intelligence is changing the way healthcare networks do business and physicians perform their routine activities from medical transcription to robot-assisted surgery. Although the more mature use-cases for AI in healthcare are those built on algorithms that have applications in various other industries (namely white-collar automation), we believe that in the coming three to five years, AI solutions for healthcare will become increasingly specialized to individual use-cases.
AI hardware is a fast-growing interest among tech media, and there is a lot of opportunity for computer hardware developers when it comes to building chipsets for AI. That said, margins for AI chipsets can differ wildly depending on the use-case for which they’re being built.